Confusion Matrix for evaluation of model

A Confusion matrix is used for evaluating the performance of a classification model. The matrix compares the actual target values with those predicted by the machine learning model.
It is a performance measurement for machine learning classification problem where output can be two or more classes. It is a table with 4 different combinations of predicted and actual values.
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Model perfomance when target feature is Reloads

Model perfomance when target feature is Product

Model perfomance when target feature is Platform

Inference: We can observe from the above plot that Average Page Depth, event_count, Time Spent per Visit, Page Views and Page loads have higher correlation with the Navigation Difficultiles.

Further Elaboration of Correlation:

The correlation coefficient has values between -1 to 1.

A value closer to 0 implies weaker correlation (exact 0 implying no correlation)
A value closer to 1 implies stronger positive correlation 
A value closer to -1 implies stronger negative correlation